Hi Liquan,
Thanks. I was running this in spark-shell. I was able to resolve this issue by
creating an app and then submitting it via spark-submit in yarn-client mode. I
have seen this happening before as well -- submitting via spark-shell has
memory issues. The same code then works fine when submitted as an app in
spark-submit yarn-client mode. I am not sure whether this is due to difference
between spark-shell and spark-submit or yarn vs non-yarn mode.....
Date: Wed, 24 Sep 2014 22:13:35 -0700
Subject: Re: MLUtils.loadLibSVMFile error
From: liquan...@gmail.com
To: ssti...@live.com
CC: so...@cloudera.com; user@spark.apache.org
Hi Sameer,
I think there are two things that you can do1) What is your current
driver-memory or executor-memory, you can try to Increate driver-memory or
executor-memory to see if that solves your problem. 2) How many features in
your data? Two many features may create a large number of temp objects, which
may also cause GC to happen.
Hope this helps!Liquan
On Wed, Sep 24, 2014 at 9:50 PM, Sameer Tilak <ssti...@live.com> wrote:
Hi All,I was able to solve this formatting issue. However, I have another
question. When I do the following,
val examples: RDD[LabeledPoint]
=MLUtils.loadLibSVMFile(sc,"structured/results/data.txt")
I get java.lang.OutOfMemoryError: GC overhead limit exceeded error. Is it
possible to specify the number of partitions explicitly?
I want to add that this dataset is sparse and is fairly small -- ~250 MB.
Error log:
14/09/24 21:41:02 ERROR Executor: Exception in task ID
0java.lang.OutOfMemoryError: GC overhead limit exceeded at
java.util.regex.Pattern.compile(Pattern.java:1655) at
java.util.regex.Pattern.<init>(Pattern.java:1337) at
java.util.regex.Pattern.compile(Pattern.java:1022) at
java.lang.String.split(String.java:2313) at
java.lang.String.split(String.java:2355) at
scala.collection.immutable.StringLike$class.split(StringLike.scala:201) at
scala.collection.immutable.StringOps.split(StringOps.scala:31) at
org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:80)
at
org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) at
scala.collection.TraversableLike$class.map(TraversableLike.scala:244) at
scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108) at
org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79) at
org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76) at
scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at
scala.collection.Iterator$class.foreach(Iterator.scala:727) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at
org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107) at
org.apache.spark.rdd.RDD.iterator(RDD.scala:227) at
org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31) at
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at
org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111) at
org.apache.spark.scheduler.Task.run(Task.scala:51) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187) at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)14/09/24
21:41:02 ERROR ExecutorUncaughtExceptionHandler: Uncaught exception in thread
Thread[Executor task launch worker-0,5,main]java.lang.OutOfMemoryError: GC
overhead limit exceeded at
java.util.regex.Pattern.compile(Pattern.java:1655) at
java.util.regex.Pattern.<init>(Pattern.java:1337) at
java.util.regex.Pattern.compile(Pattern.java:1022) at
java.lang.String.split(String.java:2313) at
java.lang.String.split(String.java:2355) at
scala.collection.immutable.StringLike$class.split(StringLike.scala:201) at
scala.collection.immutable.StringOps.split(StringOps.scala:31) at
org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:80)
at
org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) at
scala.collection.TraversableLike$class.map(TraversableLike.scala:244) at
scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108) at
org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79) at
org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76) at
scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at
scala.collection.Iterator$class.foreach(Iterator.scala:727) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at
org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107) at
org.apache.spark.rdd.RDD.iterator(RDD.scala:227) at
org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31) at
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at
org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111) at
org.apache.spark.scheduler.Task.run(Task.scala:51) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187) at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> From: so...@cloudera.com
> Date: Wed, 24 Sep 2014 23:07:27 +0100
> Subject: Re: MLUtils.loadLibSVMFile error
> To: ssti...@live.com
>
> Well, why not show some of the file? it's pretty certain there is a
> problem with the format. The large repeated stack trace doesn't say
> anything more.
>
> On Wed, Sep 24, 2014 at 11:02 PM, Sameer Tilak <ssti...@live.com> wrote:
> > Hi All,
> >
> >
> > When I try to load dataset using MLUtils.loadLibSVMFile, I have the
> > following problem. Any help will be greatly appreciated!
> >
> >
> >
> > Code snippet:
> >
> >
> > import org.apache.spark.mllib.regression.LabeledPoint
> >
> > import org.apache.spark.mllib.util.MLUtils
> >
> > import org.apache.spark.rdd.RDD
> >
> > import org.apache.spark.mllib.regression.LinearRegressionWithSGD
> >
> >
> > val examples: RDD[LabeledPoint] =
> > MLUtils.loadLibSVMFile(sc,"structured/results/data.txt")
> >
> >
> > stacktrace:
> >
> >
> > 14/09/24 15:00:49 ERROR Executor: Exception in task ID 0
> > java.lang.ArrayIndexOutOfBoundsException: 1
> > at
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:82)
> > at
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
> > at
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> > at
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> > at
> > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> > at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> > at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> > at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
> > at org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79)
> > at org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76)
> > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> > at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> > at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> > at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> > at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> > at org.apache.spark.scheduler.Task.run(Task.scala:51)
> > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> > at
> > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> > at
> > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> > at java.lang.Thread.run(Thread.java:744)
> > 14/09/24 15:00:49 ERROR Executor: Exception in task ID 1
> > java.lang.ArrayIndexOutOfBoundsException: 1
> > at
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:82)
> > at
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
> > at
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> > at
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> > at
> > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> > at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> > at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> > at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
> > at org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79)
> > at org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76)
> > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> > at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> > at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> > at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> > at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> > at org.apache.spark.scheduler.Task.run(Task.scala:51)
> > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> > at
> > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> > at
> > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> > at java.lang.Thread.run(Thread.java:744)
> > 14/09/24 15:00:49 WARN TaskSetManager: Lost TID 0 (task 0.0:0)
> > 14/09/24 15:00:49 WARN TaskSetManager: Loss was due to
> > java.lang.ArrayIndexOutOfBoundsException
> > java.lang.ArrayIndexOutOfBoundsException: 1
> > at
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:82)
> > at
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
> > at
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> > at
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> > at
> > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> > at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> > at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> > at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
> > at org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79)
> > at org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76)
> > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> > at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> > at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> > at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> > at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> > at org.apache.spark.scheduler.Task.run(Task.scala:51)
> > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> > at
> > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> > at
> > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> > at java.lang.Thread.run(Thread.java:744)
> > 14/09/24 15:00:49 ERROR TaskSetManager: Task 0.0:0 failed 1 times; aborting
> > job
> > 14/09/24 15:00:49 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks
> > have all completed, from pool
> > 14/09/24 15:00:49 INFO DAGScheduler: Failed to run reduce at
> > MLUtils.scala:95
> > 14/09/24 15:00:49 INFO TaskSchedulerImpl: Cancelling stage 0
> > 14/09/24 15:00:49 INFO TaskSetManager: Loss was due to
> > java.lang.ArrayIndexOutOfBoundsException: 1 [duplicate 1]
> > 14/09/24 15:00:49 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks
> > have all completed, from pool
> > org.apache.spark.SparkException: Job aborted due to stage failure: Task
> > 0.0:0 failed 1 times, most recent failure: Exception failure in TID 0 on
> > host localhost: java.lang.ArrayIndexOutOfBoundsException: 1
> >
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:82)
> >
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
> >
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> >
> > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> >
> > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> > scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> >
> > scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> > scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
> >
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79)
> >
> > org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76)
> > scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> > scala.collection.Iterator$class.foreach(Iterator.scala:727)
> > scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> >
> > scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> >
> > scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> > org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107)
> > org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
> > org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
> > org.apache.spark.scheduler.Task.run(Task.scala:51)
> >
> > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
> >
> > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> >
> > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> > java.lang.Thread.run(Thread.java:744)
> > Driver stacktrace:
> > at
> > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1033)
> > at
> > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1017)
> > at
> > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1015)
> > at
> > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> > at
> > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1015)
> > at
> > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:633)
> > at
> > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:633)
> > at scala.Option.foreach(Option.scala:236)
> > at
> > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:633)
> > at
> > org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1207)
> > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> > at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> > at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> > at
> > akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
> > at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> > at
> > scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> > at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> > at
> > scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
--
Liquan Pei
Department of Physics
University of Massachusetts Amherst
